出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Spectroscopy or hyperspectral imaging consists in the acquisition, analysis, and extraction ofthe spectral information measured on a specific region or object using an airborne or satellitedevice. Hyperspectral imaging has become an active field of research recently. One way ofanalysing such data is through clustering. However, due to the high dimensionality of the dataand the small distance between the different material signatures, clustering such a data is achallenging task.In this paper, we empirically compared five clustering techniques in differenthyperspectral data sets. The considered clustering techniques are K-means, K-medoids, fuzzy Cmeans,hierarchical, and density-based spatial clustering of applications with noise. Four datasets are used to achieve this purpose which is Botswana, Kennedy space centre, Pavia, andPavia University. Beside the accuracy, we adopted four more similarity measures: Randstatistics, Jaccard coefficient, Fowlkes-Mallows index, and Hubert index. According toaccuracy, we found that fuzzy C-means clustering is doing better on Botswana and Pavia datasets, K-means and K-medoids are giving better results on Kennedy space centre data set, andfor Pavia University the hierarchical clustering is better.
